Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
This addresses the problem of limited reasoning accuracy in LLMs for complex logical problems, representing an incremental advancement over existing methods like Chain-of-Thought and Tree-of-Thought.
The paper tackles the challenge of LLMs struggling with complex reasoning by proposing the Forest-of-Thought framework, which integrates multiple reasoning trees and dynamic self-correction to enhance accuracy and efficiency, achieving significant improvements in solving complex tasks.
Large Language Models (LLMs) have demonstrated remarkable abilities across various language tasks, but solving complex reasoning problems remains a significant challenge. While existing methods, such as Chain-of-Thought (CoT) and Tree-of-Thought (ToT), enhance reasoning by decomposing problems or structuring prompts, they typically perform a single pass of reasoning and may fail to revisit flawed paths, compromising accuracy. To address this limitation, we propose a novel reasoning framework called Forest-of-Thought (FoT), which integrates multiple reasoning trees to leverage collective decision-making for solving complex logical problems. FoT employs sparse activation strategies to select the most relevant reasoning paths, improving both efficiency and accuracy. Additionally, we introduce a dynamic self-correction strategy that enables real-time error correction, along with consensus-guided decision-making strategies to optimize both correctness and computational resources. Experimental results demonstrate that the FoT framework, combined with these strategies, significantly enhances the reasoning capabilities of LLMs, enabling them to solve complex tasks with greater precision and efficiency. Code will be available at https://github.com/iamhankai/Forest-of-Thought.